A scalable RC architecture for mean-shift clustering

摘要

The mean-shift algorithm provides a unique non-parametric and unsupervised clustering solution to image segmentation and has a proven record of very good performance for a wide variety of input images. It is essential to image processing because it provides the initial and vital steps to numerous object recognition and tracking applications. However, image segmentation using mean-shift clustering is widely recognized as one of the most compute-intensive tasks in image processing, and suffers from poor scalability with respect to the image size (N pixels) and number of iterations (k): O(kN